class_docstr: Bayesian regression model. If poly_degree is specified the features will
  be transformed to with a polynomial basis function, which allows for polynomial
  regression. Assumes Normal prior and likelihood for the weights and scaled inverse
  chi-squared prior and likelihood for the variance of the weights.
  
  Parameters:
  -----------
  n_draws: float
      The number of simulated draws from the posterior of the parameters.
  mu0: array
      The mean values of the prior Normal distribution of the parameters.
  omega0: array
      The precision matrix of the prior Normal distribution of the parameters.
  nu0: float
      The degrees of freedom of the prior scaled inverse chi squared distribution.
  sigma_sq0: float
      The scale parameter of the prior scaled inverse chi squared distribution.
  poly_degree: int
      The polynomial degree that the features should be transformed to. Allows
      for polynomial regression.
  cred_int: float
      The credible interval (ETI in this impl.). 95 => 95% credible interval of the posterior
      of the parameters.
  
  Reference:
      https://github.com/mattiasvillani/BayesLearnCourse/raw/master/Slides/BayesLearnL5.pdf
code: def __init__(
      self,
      n_draws,
      mu0,
      omega0,
      nu0,
      sigma_sq0,
      poly_degree=0,
      cred_int=95,
  ):
      self.w = None
      self.n_draws = n_draws
      self.poly_degree = poly_degree
      self.cred_int = cred_int
  
      # Prior parameters
      self.mu0 = mu0
      self.omega0 = omega0
      self.nu0 = nu0
      self.sigma_sq0 = sigma_sq0
  
file_path: ML-From-Scratch/mlfromscratch/supervised_learning/bayesian_regression.py
imports: from __future__ import print_function, division
  import numpy
  from scipy.stats import chi2, multivariate_normal
  from mlfromscratch.utils import mean_squared_error, train_test_split, polynomial_features
label_desc: Use image node_id 1 to create a new BayesianRegression object from inherited base classes: object with example: obj = BayesianRegression(n_draws, mu0, omega0, nu0, sigma_sq0, poly_degree, cred_int)